Rainfall is often used as an instrument for income in largely agrarian economies (if you are unfamiliar with the term “instrument” please wait a bit). One prominent 2004 example, by Miguel, Satyanath, and Sergenti, investigates the influence of economic growth on civil conflict in Sub-Saharan Africa. Conflicts are often preceded by a growth slowdown or economic contraction – the authors estimate a 5 percentage point drop in economic growth is followed in the next year by a 12 percentage point increase in the likelihood of economic conflict. Given the temporal ordering, is tempting to point to a causal role for the economic shock in fostering conflict. The problem, as recognized by the authors, is that people may anticipate a civil conflict and adjust activity accordingly. So even though the economic decline precedes conflict, it does not necessarily cause such conflict.

One way to tease a causal estimate out of what is otherwise a mere correlation between growth and conflict is – if the researcher is very lucky – the inclusion of an instrument for growth. This is a variable that determines income but does not affect the likelihood of conflict except through the income channel. Rainfall is a popular instrument for income and other economic variables in many settings since local rainfall directly affects agricultural production but itself is unaffected by economic life (i.e. it is exogenous). Miguel and co-authors instrument for national income with rainfall and find that the strong correlation between an economic contraction and subsequent conflict persists, thus suggesting that this relationship is indeed causal (or at least contains a strong causal component).

The key assumption in this analysis is that rain only affects conflict through the direct effect of rain on income generating activities. This assumption is known as the “exclusion restriction” of an instrument. In general it’s very difficult to test the validity of the exclusion restriction directly, although Miguel and co-authors do their best. They note that the restriction may be invalid if rain “makes it difficult for both government and rebel forces to engage each other in combat… because of more difficult transportation conditions.” With some data on usable road networks, they find that rainfall shocks have no impact on the extent of usable roads.

While suggestive, I’d say this exclusion restriction test is not definitive. Rain certainly leads to higher crop yields and hence more growth in agrarian economies, but heavy seasonal rain also makes travel and transport more difficult – in Zambia, where I’ve been working for many years, a heavy rain season will cut off whole districts from the rest of the country for months on end due to river flooding. Bad weather also makes the assembly of large groups difficult to coordinate. If rain does affect movement and assembly, then it may also directly affect the incidence of conflict. After all Frederick II of Prussia wrote to his generals: “It is always necessary to shape operational plans… on estimates of the weather”.

Enter the short paper by Heather Sarsons that directly investigates the exclusion restriction assumption, albeit in a different setting. Sarsons investigates rainfall and civil conflict (in the form of internecine riots) in India. She exploits the fact that agricultural production in some districts in India is largely rain-fed while in other districts the majority of agriculture is irrigated through the use of dams. As expected, agricultural wages in rain-fed districts are responsive to rain and, when rain is sufficient and wages high, internecine conflict is also significantly lower. So far this story is completely consistent with previous work such as Miguel and co-authors.

However in dam-fed districts there is no relation between rainfall and agricultural wages – after all people build dams to protect themselves from the vicissitudes of short-term fluctuations in rain. Nevertheless internecine riots are still significantly lower during rainy periods in the dam-fed districts. So here rainy periods don’t affect wages but still result in lower conflict. This of course suggests that rain affects conflict through multiple channels and not just through its effect on economic activity. If this is the case then the exclusion restriction is invalid.

Perhaps the story is as simple as heavy rainfall slows transport and deters group assemblies. After all nobody likes to go out in the rain, even to a riot. So if we are tempted to use rain as an instrument, it could be a great one but let’s not forget the wisdom of an authority on many aspects of life – the Beatles: “if the rain comes they run and hide their heads…” (note, you’ll need volume for this link, so don’t open in a shared office).

Three caveats to all this:

1.The effects that Sarsons observes can be due to conflict spill-overs from rain-fed to dam-fed districts – perhaps rain doesn’t directly reduce conflict in dam-fed districts but these areas still benefit from the rain induced conflict reduction in rain-fed districts. She has determined that rain doesn’t drive migration but there may be other channels of conflict transmission. I am sure that she is currently investigating this issue.

2.Sarsons only investigates the contemporaneous effect of rain on conflict while Miguel et al. look at both contemporaneous and lagged effects. It’s possible that lagged rainfall may still be a valid instrument even if current rainfall violates the exclusion restriction.

3.There is a question over exactly how to model weather shocks. Since I am currently looking at weather shocks and mortality, I’ll add my general thoughts in a future post.

Comments

So, there is a positive relationship between rainfall and income in largely agrarian economies. So, less rain may lead to greater civil conflict. But you are also suggesting that more rain leads to less civil conflict: no one wants to go outside in the rain. So, maybe there's some degree of endogeneity, but these relationships aren't very clear.
It seems that the first relationship is largely a function of long-term cumulative rainfall over a growing season, which may last 3-6 months. Whereas the second relationship seems to depend on a more immediate relationship between today's rainfall and today's demonstrations, or perhaps rainfall over the past week that makes travel conditions difficult. Since most demonstrations occur in urban areas, however, is the concern about travel conditions fully justified?
Either way, I wouldn't use the same rainfall variable in both analyses.

Thanks for the comment! Most of these studies are constrained to look at annual aggregates of rainfall and conflict incidence. I agree that higher frequency data would be able to better distinguish between the oossible causal channels.

In my grad metrics class, I actually had my students perform a similar identification check with Miguel et al.'s own data. If you restrict the estimation to countries for which the first stage relationships do not hold, then the reduced form correlation between rainfall growth and conflict is zero. So no reason to reject exclusion on the basis of this test. This points to the fact that the India results may not be relevant to the Miguel et al. findings, which are driven by conflict dynamics in Sub-Saharan Africa.